Why AI Agents Need an Open Protocol
In most enterprises, AI agents emerge wherever they are needed: an inventory agent in logistics, a research agent in marketing, an order agent in sales. Each of these agents is built on its own framework – LangChain, CrewAI, or an in-house platform – and lives inside its own ecosystem. The moment two agents need to talk to each other, integration overhead kicks in: proprietary APIs, custom data formats, manually wired workflows.
This is exactly where the Agent2Agent protocol (A2A) comes in. It is an open standard for communication between AI agents – regardless of who built them or what framework they run on. Originally introduced in April 2025 by Google and a group of technology partners, A2A is now developed as an open-source project under the Linux Foundation. It positions itself as a messaging layer for multi-agent systems – essentially a common language that lets agents from different vendors talk to each other despite different architectures.
A2A vs. MCP: Two Protocols, Two Jobs
A2A is often mentioned alongside Anthropic's Model Context Protocol (MCP) – but the two are not competitors, they are complementary:
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MCP standardizes how an AI application talks to external tools, APIs, and data sources.
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A2A standardizes how AI agents talk to each other and collaborate on tasks.
An example from retail: an inventory agent uses MCP to query stock levels from the ERP system. When it detects low stock, it uses A2A to delegate to an internal ordering agent, which in turn negotiates over A2A with the agents of external suppliers. MCP governs access to data and tools; A2A governs collaboration between the agents themselves.
The Building Blocks of A2A
A2A follows a clean client-server model with a small set of well-defined building blocks:
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A2A client (client agent): the application or agent delegating a task.
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A2A server (remote agent): the agent that performs the task – exposed through an HTTP endpoint.
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Agent card: a publicly accessible JSON file describing the agent – name, version, endpoint, supported modalities, authentication, and capabilities. The agent card is essentially the agent's resume and makes it discoverable to others.
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Task: a unit of work with its own ID and a defined lifecycle (submitted, working, input required, completed, failed).
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Message and part: the smallest unit of communication. A message contains one or more parts – as TextPart, FilePart, or structured DataPart in JSON.
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Artifact: the tangible result returned by the remote agent – a document, an image, a table, or structured data.
How A2A Works in Three Steps
Collaboration between two agents follows a three-stage workflow:
1. Discovery
The client agent searches for suitable remote agents and reads their agent cards. Based on the capabilities described there, it decides which agent is best suited for the task.
2. Authentication
Before the first call, the client agent authenticates according to the security scheme declared on the agent card. A2A supports established OpenAPI-aligned mechanisms – API keys, OAuth 2.0, OpenID Connect Discovery. The remote agent is then responsible for authorization.
3. Communication
The actual collaboration runs over HTTPS and JSON-RPC 2.0. The client agent sends a task, the remote agent processes it, asks for additional input if needed, and finally returns a message together with any generated artifacts. For long-running tasks, A2A supports asynchronous push notifications to client-provided webhooks; for streaming updates and large outputs, it uses Server-Sent Events (SSE).
Why A2A Matters for Enterprises
A2A solves three problems that inevitably appear in productive multi-agent scenarios:
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Privacy through opacity: agents collaborate without exposing their internal models, prompts, or tool implementations. That protects intellectual property and sensitive data – also across organizational boundaries.
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Seamless integration: A2A is built on proven standards – HTTP, JSON-RPC, SSE. It fits into existing stacks without requiring a new infrastructure layer.
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Enterprise-grade security: the protocol was designed with authentication and authorization in mind from day one and is compatible with common identity concepts.
What Organizations Should Do Now
A2A is still young, but the direction is clear: multi-agent architectures will become the norm – and they only work if agents can discover, trust, and structurally collaborate with each other. Three steps to prepare realistically:
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Build an agent inventory: Which agents already exist, what capabilities do they have, which identities and permissions do they need?
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Standardize interfaces: For new agents, define agent cards from the start, document the security scheme, and provide A2A-compatible endpoints.
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Plan for governance: Who is allowed to invoke which agent, which data may flow over A2A, how are tasks and artifacts audited?
Conclusion: From Isolated Copilots to a Connected Agent Ecosystem
A2A creates what MCP delivered for tool access – just one layer up: an open, unified way for AI agents to work together. For enterprises, it means the opportunity to bring agents from different vendors, frameworks, and business units into one consistent, secure, and auditable agent ecosystem – without locking themselves to a single provider.
The strategic question is no longer whether multi-agent systems will go into production, but how quickly your own architecture will be ready for them – and A2A is one of the most important building blocks to get there.